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It's so excellent how many moats are just getting obliterated. I absolutely have been a real snarky hater against AI, as a horrible fuedal unobservable black b

It's so excellent how many moats are just getting obliterated. I absolutely have been a real snarky hater against AI, as a horrible fuedal unobservable black box that has way too much power in the world. But open source has been doing amazing at reading the papers & reproducing & it's glorious to see. Amazing examples of a peership culture in action. Rising each other up is so divine. Share the knowledge & means.

philipkglass 3y ago
I recently replaced an image classification pipeline that leaned heavily on classical computer vision techniques (like you'd find in OpenCV) with a neural network based approach using open models. There were about 4 years of developer effort invested in that old pipeline and I got better results with 2 months of effort invested in the new NN based system. Later this year I plan on revamping an old NLP system with even more man-years of effort invested in it. I think I can beat it with neural networks too. The main reason I haven't started already is that open language model progress is so fast that I expect significantly better building blocks in 4 months. Using these new tools feels magical, particularly when you have experienced how much effort it took to get half-as-good results with older techniques.
- Fordec 3y ago
  Any NN tips? I have my legacy pipelines, but haven't put the effort to migrate yet even though I know I should.
  - lettergram 3y ago
    Isolate the prior logic into functional components. Find the inputs and outputs to each box. Identify which component ML can replace. Replace one after the next, sometimes merging if the ML can do it all on the gpu
  - chintler 3y ago
    I've modernized a lot of legacy pipelines and can help you if you want.
  - philipkglass 3y ago
    The ML/DL software ecosystem is dominated by Python. Python's dependency management can be especially tricky, so try to limit the dependencies you're pulling in for the final deployed artifact if your final artifact is also Python based. CUDA can be difficult to set up correctly on your personal development machine and if you rely on it you're also limiting the development machines that other people can use. You're limiting the deployment options and the CI options. This may differ if you work at a larger company that has a team specializing in these things, but I had to work out everything from initial proof-of-concept to final deployment. Some applications absolutely need the higher performance from GPU execution but it's worth seeing if you can get away with CPU-only execution because it avoids operational complications. I used the ONNX runtime (as this DeepFilterNet project does, indirectly, according to a top level comment by WiSaGaN) and I was able to get adequate inference speed running on plain CPU. It's a small Python service that just wraps the inference logic with a command interface and a connection to Redis. It takes protocol buffer inputs from a Redis based queue, does a little bit of control logic followed by inference, and writes the results as protocol buffers to another Redis based queue. The only ML libraries I have on the Python side are the ones for ONNX. This saved over a gigabyte (!) of transitive dependencies compared to the first proof-of-concept I had that relied on PyTorch for runtime inference. Final advice: you actually don't need to know much theory to start doing something useful. I hadn't studied neural networks since graduate school 20 years ago so my theory is hopelessly outdated. I just started hacking together a little demo for myself and it was good enough that I was encouraged to take it all the way to production.
    - aurbano 3y ago
      We recently migrated to Poetry [1] for dependency management and so far it's been a breath of fresh air - it feels like what Python deps should've always been! You can even have dependency groups, to separate main/dev dependencies for instance. It also brings env management, and plays very nicely with Docker if you use containers. [1] https://python-poetry.org https://python-poetry.org
      - KRAKRISMOTT 3y ago
        Good luck using poetry for GPU accelerated ML dependencies
- chaxor 3y ago
  To quote Lukaz Kaiser in his famous talk on 2017: "stupidity is all you need"
est31 3y ago
Note that this isn't just a paper reproduction but a new paper in and of itself (the github is by one of the paper's authors). This is unbelievably amazing. I wonder how it compares to rnnoise, which is also open source and also targets real time settings.
atoav 3y ago
As someone who worked professionally using top of the line audio denoising for speech in cinema productions I have to say that I am underwhelmed by the results. This is very similar to what traditional algorithms would have achieved a decade ago. Of course there might be potential for improvement there, maybe it is more performant or was developed way faster etc. But just listening to the audible result is not too cinvincing — yet.

philipkglass 3y ago

I recently replaced an image classification pipeline that leaned heavily on classical computer vision techniques (like you'd find in OpenCV) with a neural network based approach using open models. There were about 4 years of developer effort invested in that old pipeline and I got better results with 2 months of effort invested in the new NN based system. Later this year I plan on revamping an old NLP system with even more man-years of effort invested in it. I think I can beat it with neural networks too. The main reason I haven't started already is that open language model progress is so fast that I expect significantly better building blocks in 4 months. Using these new tools feels magical, particularly when you have experienced how much effort it took to get half-as-good results with older techniques.

Fordec 3y ago
Any NN tips? I have my legacy pipelines, but haven't put the effort to migrate yet even though I know I should.
- lettergram 3y ago
  Isolate the prior logic into functional components. Find the inputs and outputs to each box. Identify which component ML can replace. Replace one after the next, sometimes merging if the ML can do it all on the gpu
- chintler 3y ago
  I've modernized a lot of legacy pipelines and can help you if you want.
- philipkglass 3y ago
  The ML/DL software ecosystem is dominated by Python. Python's dependency management can be especially tricky, so try to limit the dependencies you're pulling in for the final deployed artifact if your final artifact is also Python based. CUDA can be difficult to set up correctly on your personal development machine and if you rely on it you're also limiting the development machines that other people can use. You're limiting the deployment options and the CI options. This may differ if you work at a larger company that has a team specializing in these things, but I had to work out everything from initial proof-of-concept to final deployment. Some applications absolutely need the higher performance from GPU execution but it's worth seeing if you can get away with CPU-only execution because it avoids operational complications. I used the ONNX runtime (as this DeepFilterNet project does, indirectly, according to a top level comment by WiSaGaN) and I was able to get adequate inference speed running on plain CPU. It's a small Python service that just wraps the inference logic with a command interface and a connection to Redis. It takes protocol buffer inputs from a Redis based queue, does a little bit of control logic followed by inference, and writes the results as protocol buffers to another Redis based queue. The only ML libraries I have on the Python side are the ones for ONNX. This saved over a gigabyte (!) of transitive dependencies compared to the first proof-of-concept I had that relied on PyTorch for runtime inference. Final advice: you actually don't need to know much theory to start doing something useful. I hadn't studied neural networks since graduate school 20 years ago so my theory is hopelessly outdated. I just started hacking together a little demo for myself and it was good enough that I was encouraged to take it all the way to production.
  - aurbano 3y ago
    We recently migrated to Poetry [1] for dependency management and so far it's been a breath of fresh air - it feels like what Python deps should've always been! You can even have dependency groups, to separate main/dev dependencies for instance. It also brings env management, and plays very nicely with Docker if you use containers. [1] https://python-poetry.org https://python-poetry.org
    - KRAKRISMOTT 3y ago
      Good luck using poetry for GPU accelerated ML dependencies
chaxor 3y ago
To quote Lukaz Kaiser in his famous talk on 2017: "stupidity is all you need"

est31 3y ago

Note that this isn't just a paper reproduction but a new paper in and of itself (the github is by one of the paper's authors). This is unbelievably amazing. I wonder how it compares to rnnoise, which is also open source and also targets real time settings.

atoav 3y ago

As someone who worked professionally using top of the line audio denoising for speech in cinema productions I have to say that I am underwhelmed by the results. This is very similar to what traditional algorithms would have achieved a decade ago. Of course there might be potential for improvement there, maybe it is more performant or was developed way faster etc. But just listening to the audible result is not too cinvincing — yet.